A QUALITY ASSURANCE/QUALITY CONTROL ANALYSIS OF RAINFALL DATA COLLECTED BY VOLUNTEERS IN TUCSON, ARIZONA FOR THE RAINLOG.ORG PROGRAM
AuthorRupprecht, Candice Lea
AdvisorWoodard, Gary C.
Maddock, III, Thomas
Committee ChairWoodard, Gary C.
Maddock, III, Thomas
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractScientists now recognize how quickly environmental conditions are changing, yet to monitor and understand these spatially distributed changes more dispersed quantitative and qualitative data are needed than ever before. The need for more comprehensive and robust data has created the burgeoning field of citizen science, which engages volunteers to monitor environmental changes and report this information to scientists. Precipitation monitoring networks like RainLog.org are considered one of the oldest types of citizen science with many networks in existence for over 100 years. RainLog.org is a more modern version of these original networks and was developed in response to a need to better characterize precipitation events and provide stakeholders with more robust precipitation totals and distributions throughout Arizona.RainLog.org is a statewide precipitation monitoring network that relies on volunteers across Arizona to report daily precipitation into an online reporting system. To ensure that these data are reliable, a quality assurance and quality control analysis (QA/QC) was completed on a subset of gauges in the Tucson area. Results indicate that although there are many errors inherent with any precipitation network, whether volunteer or scientist driven, these errors are for the most part identified using basic interpolation methods. This paper analyzes a range of user reporting and gauge type errors, discusses the significance of each error type and provides recommendations for mitigating reporting errors in any citizen science network.